Interpretive Summary: Food safety is dependent upon the ability to detect and differentiate foodborne pathogens that cause severe outbreaks. Major outbreak in food is caused by foodborne pathogenic bacteria such as Salmonella, E. coli., Staphylococcus, Shigella. Since conventional microbiological methods are labor intensive and time consuming, rapid methods to detect pathogens in food matrices are needed for food processing industries to maintain food safety. Optical methods including Raman spectroscopy are good candidate for rapid pathogen detection. Raman spectroscopy technique provides spectral information furnished by molecular vibrations that are provided by mid- and near-infrared spectra. The intensity of Raman scattering can be enhanced with a suitable roughness of metal surface with silver, gold or copper, resulting in surface enhanced Raman Scattering (SERS). SERS technique is widely applied in identification of foodborne pathogens; because SERS scattering gives molecular vibration and brings molecular band characteristics in fingerprint region to identify bacterial serotypes as well as species. However, aggregation of silver colloids often happens in SERS substrate which produces wide variation in signal enhancement in biological sample identification. While, biopolymer-based nanosubstrates show promising for foodborne pathogen detection with SERS. In this study, we observed the feasibility of rapid identification of foodborne pathogens such as Salmonella Typhimurium, E. coli, Listeria innocua and Staphylococcus aureus that were isolated from chicken rinse using a SERS method with biopolymer encapsulated with silver nanoparticles.

Technical Abstract:
A biopolymer encapsulated with silver nanoparticles was prepared using polyvinyl alcohol (PVA) solution, silver nitrate, and trisodium citrate. Biopolymer based nanosubstrates were deposited on a mica sheet for SERS. Fresh cultures of Salmonella Typhimurium, Escherichia coli, Staphylococcus aureus and Listeria innocua were washed from chicken rinse and suspended in 10 mL of sterile deionized water. Approximately 5 µL of bacterial suspensions were placed on substrates individually and exposed to 785 nm HeNe laser excitation. SERS spectral data between 400 and 1800 cm-1 were collected from 15 different spots on the substrate for each sample. Three replicate samples from bacteria were prepared and scanned. Principal component analysis (PCA) model was developed to classify foodborne bacteria. PC1 identified 96% of the variation among the given bacterial species; while PC2 identified 3%, resulted in a total of 99% classification accuracy. Soft independent modeling of class analogies (SIMCA) of validation set gave an overall classification accuracy of 97%. Major differences between gram-negative and gram-positive bacteria were noted around the nucleic acids and amino acids structure information between 1200 cm-1 and 1700 cm-1 and at the fingerprint region between 400 cm-1 and 700 cm-1. Biopolymer encapsulated with silver nanoparticles could be potential SERS substrates for rapid detection of foodborne pathogen.